4 research outputs found
UPPLIED: UAV Path Planning for Inspection through Demonstration
In this paper, a new demonstration-based path-planning framework for the
visual inspection of large structures using UAVs is proposed. We introduce
UPPLIED: UAV Path PLanning for InspEction through Demonstration, which utilizes
a demonstrated trajectory to generate a new trajectory to inspect other
structures of the same kind. The demonstrated trajectory can inspect specific
regions of the structure and the new trajectory generated by UPPLIED inspects
similar regions in the other structure. The proposed method generates
inspection points from the demonstrated trajectory and uses standardization to
translate those inspection points to inspect the new structure. Finally, the
position of these inspection points is optimized to refine their view. Numerous
experiments were conducted with various structures and the proposed framework
was able to generate inspection trajectories of various kinds for different
structures based on the demonstration. The trajectories generated match with
the demonstrated trajectory in geometry and at the same time inspect the
regions inspected by the demonstration trajectory with minimum deviation. The
experimental video of the work can be found at https://youtu.be/YqPx-cLkv04.Comment: Accepted for publication in IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS 2023), Detroit, Michigan, US
DynaCon: Dynamic Robot Planner with Contextual Awareness via LLMs
Mobile robots often rely on pre-existing maps for effective path planning and
navigation. However, when these maps are unavailable, particularly in
unfamiliar environments, a different approach become essential. This paper
introduces DynaCon, a novel system designed to provide mobile robots with
contextual awareness and dynamic adaptability during navigation, eliminating
the reliance of traditional maps. DynaCon integrates real-time feedback with an
object server, prompt engineering, and navigation modules. By harnessing the
capabilities of Large Language Models (LLMs), DynaCon not only understands
patterns within given numeric series but also excels at categorizing objects
into matched spaces. This facilitates dynamic path planner imbued with
contextual awareness. We validated the effectiveness of DynaCon through an
experiment where a robot successfully navigated to its goal using reasoning.
Source code and experiment videos for this work can be found at:
https://sites.google.com/view/dynacon.Comment: Submitted to ICRA 202